f2aa9d15-62d6-4c39-9fbe-b83434424752.jpg

Massive Machine Learning – Dr Paul Pallath at Aegis Data Science Congress 2017


Dr Paul Pallath delivered key talk at Data Science Congress 2017 on Massive Machine Learning Whether businesses want to use data to make the world a bette

Sourced through Scoop.it from: www.youtube.com

c8fe1543-2445-4184-ab70-c710cba4aa03.jpg

Words to go: Google cloud machine learning services

Google cloud machine learning services offer IT teams various features, such as speech-to-text and image analysis. Learn these key terms before adoption.

Sourced through Scoop.it from: searchcloudcomputing.techtarget.com

8f6f5c27-9d87-4063-9ba2-e08ee94b26f9.jpg

Machine Learning, Deep Learning 101

This article describes the growing relevance of Machine Learning used in various kinds of analytics along with an overview of Deep Learning. It provides an end-to-end process for using Machine Learning and Deep Learning and the options for getting started on IBM® Power Systems™.

Sourced through Scoop.it from: www.ibm.com

good read

8b804417-6b56-4127-b921-86a9147e6971.jpg

Dr Usama Fayyad, 1st Chief Data Officer speaking on Data Science at Aegis Data Science Congress 2017


Dr Usama Fayyad delivered the keynote address at the Aegis Data Science Congress 2017 held in Mumbai from 5 to 8 June. DSC an initiative of Aegis School o

Sourced through Scoop.it from: www.youtube.com

must watch 

292d721c-99b4-4e43-bfc8-405f3d4512c1.jpg

How to Plan and Run Machine Learning Experiments Systematically – Machine Learning Mastery

Machine learning experiments can take a long time. Hours, days, and even weeks in some cases. This gives you a lot of time to think and plan for additional experiments to perform. In addition, the average applied machine learning project may require tens to hundreds of discrete experiments in order to find a data preparation …

Sourced through Scoop.it from: machinelearningmastery.com

nice read

10a15b6a-af72-499f-993e-5f1e4eec2c08.jpg

DEF CON 25 (2017) – Weaponizing Machine Learning – Petro, Morris – Stream – 30July2017


30 July 2017 – DEF CON 25 (2017) Dan ‘AltF4′ Petro & Ben Morris – Bishop Fox https://www.bishopfox.com Weaponizing Machine Learning: Humanity Was Overrate

Sourced through Scoop.it from: www.youtube.com

69d5b9d5-7d11-44fe-922d-1d21bb60e452.jpg

Demystifying the Black Box of Machine Learning

Nowadays, it is common to use machine learning to detect online fraud. In fact, machine learning is everywhere. Due to its independent nature and human-like intelligence qualities, machine learning does, at times, seem like an inexplicable “black box.” But truth be told, machine learning doesn’t have to be like that. Here is what you should know if you decide to give “computers the ability to learn without being explicitly programmed.” Before choosing fraud detection technologies that leverage machine learning, consider the advantages and disadvantages of the different algorithms together with the demand for transparency, prediction accuracy and ability to adjust to the rapidly changing landscape (be it fraud or others). In order to be truly successful, the specific algorithm you use, combined with your domain knowledge, can make all the difference. Here are a few algorithms and factors you should consider: Artificial Neural Network (ANN; or their more advanced counterpart – Deep Neural Nets) is considered to be a “universal approximator” as it fits almost any scenario and field. But is it the best one in every field? While Deep Neural Nets is superior to other algorithms when image or speech recognition is considered (in other words, when working with huge data sets), there are a lot of examples where ANN can produce inferior results to other classification techniques especially if the size of training sample is limited. It requires a large set of training data and is prone to over-fitting. ANN is sometimes referred to as “the second best way to solve any problem” while the best way is to actually to understand the different parameters of the problem you are trying to solve and then implement a model closely resembling reality. Another statistical method known as naïve Bayes classifier – a probabilistic supervised classifier tool – has been…

Sourced through Scoop.it from: blogs.rsa.com

21d22faa-f3cc-4b73-95f5-6252c39a5623.jpg

Machine Learning, Artificial Intelligence – And The Future Of Accounting

Machine learning and artificial intelligence are going to disrupt most professions. Here we take a look at the impact of machine learning on accounting and explore how parts of the profession will be threatened while others will be enhanced.

Sourced through Scoop.it from: www.forbes.com

7cb4f962-1fc5-45be-810d-7f4c4e612c13.jpg

Google’s Deeplearn.js brings machine learning to the browser

The open source GPU-accelerated library supports TypeScript and JavaScript, allowing you to train neural networks or run pre-trained models

Sourced through Scoop.it from: www.infoworld.com

Nice article 

Usama

Five reasons why Data Scientists are Hot!!

Five reasons why Data Scientists are Hot!! Bhupesh Daheria CEO, Aegis School of Business, Data Science, Cyber Security & Telecom I Founder – Data Science Delivered, Data Science Congress, Bell Award, mUniversity I Data Scientists-‘The Czar of the Modern World’ The Harvard Business Review claims it as one of the sexiest jobs of the 21st …

Read more